code: def test_convert_metric_value_to_float():
      float_metric_val = float(random_int(10, 50))
  
      assert (
          convert_metric_value_to_float_if_possible(float_metric_val)
          == float_metric_val
      )
  
      ndarray_val = np.random.rand(1)
      assert convert_metric_value_to_float_if_possible(
          ndarray_val
      ) == float(ndarray_val[0])
  
file_path: mlflow/tests/tracking/fluent/test_metric_value_conversion_utils.py
imports: import numpy
  import pytest
  import mlflow
  from mlflow import tracking
  from mlflow.exceptions import INVALID_PARAMETER_VALUE, ErrorCode, MlflowException
  from mlflow.tracking.fluent import start_run
  from mlflow.tracking.metric_value_conversion_utils import convert_metric_value_to_float_if_possible
  from tests.helper_functions import random_int
label_desc: Use image node_id 2 for calling a global function with example usage: test_convert_metric_value_to_float() without return types